Abstract Context: Big Data systems are a class of software systems that ingest, store,
process and serve massive amounts of heterogeneous data, from multiple sources. Despite …

Over the last decade, a variety of external file formats such as Parquet, ORC, Arrow, etc.,
have been developed to store large volumes of relational data in the cloud. As high …

When exploring big amounts of data without a clear target, providing an interactive
experience becomes really difficult, since this tentative inspection usually defeats any early …

Modern big data frameworks (such as Hadoop and Spark) allow multiple users to do large-
scale analysis simultaneously, by deploying data-intensive workflows (DIWs). These DIWs of …

Data-intensive flows deploy a variety of complex data transformations to build information
pipelines from data sources to different end users. As data are processed, these workflows …

Ad-hoc analysis implies processing data in near real-time. Thus, raw data (ie, neither
normalized nor transformed) is typically dumped into a distributed engine, where it is …

Modern big data frameworks (such as Hadoop and Spark) allow multiple users to do large-
scale analysis simultaneously. Typically, users deploy Data-Intensive Workflows (DIWs) for …

The past few years have seen a tremendous increase in often irregularly structured data that
can be represented most naturally and efficiently in the form of graphs. Making sense of …

Modern organizations produce and collect large volumes of data, that need to be processed
repeatedly and quickly for gaining business insights. For such processing, typically, Data …

Data has an undoubtedly impact on society. Storing and processing large amounts of
available data is currently one of the key success factors for an organization. In order to carry …